{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/domain-adaptive-video-segmentation-via","title":"Domain Adaptive Video Segmentation via Temporal Consistency Regularization","arxiv_id":"2107.11004","date":"2021-07-23","proceeding":"ICCV 2021 10","authors":["Dayan Guan","Jiaxing Huang","Aoran Xiao","Shijian Lu"],"abstract":"Video semantic segmentation is an essential task for the analysis and understanding of videos. Recent efforts largely focus on supervised video segmentation by learning from fully annotated data, but the learnt models often experience clear performance drop while applied to videos of a different domain. This paper presents DA-VSN, a domain adaptive video segmentation network that addresses domain gaps in videos by temporal consistency regularization (TCR) for consecutive frames of target-domain videos. DA-VSN consists of two novel and complementary designs. The first is cross-domain TCR that guides the prediction of target frames to have similar temporal consistency as that of source frames (learnt from annotated source data) via adversarial learning. The second is intra-domain TCR that guides unconfident predictions of target frames to have similar temporal consistency as confident predictions of target frames. Extensive experiments demonstrate the superiority of our proposed domain adaptive video segmentation network which outperforms multiple baselines consistently by large margins.","url_abs":"https://arxiv.org/abs/2107.11004v1","url_pdf":"https://arxiv.org/pdf/2107.11004v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"domain-adaptive-video-segmentation-via","repo_url":"https://github.com/Dayan-Guan/DA-VSN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"},{"task_slug":"video-semantic-segmentation","task_name":"Video Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2107.11004","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.11004"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Dayan-Guan/DA-VSN","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":3},"by_repo_kind":{"official":{"samples":3,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"d7bf73450e7fb5a6","entry":"get_accel_deeplab_v2","repo":"Dayan-Guan/DA-VSN","repo_kind":"official","path":"davsn/model/accel_deeplabv2.py","file_url":"https://github.com/Dayan-Guan/DA-VSN/blob/HEAD/davsn/model/accel_deeplabv2.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"d7bf73450e7fb5a6"}},{"code_sha256_prefix":"043a52d8f9ff0f65","entry":"to_numpy","repo":"Dayan-Guan/DA-VSN","repo_kind":"official","path":"davsn/domain_adaptation/train_video_UDA.py","file_url":"https://github.com/Dayan-Guan/DA-VSN/blob/HEAD/davsn/domain_adaptation/train_video_UDA.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"043a52d8f9ff0f65"}},{"code_sha256_prefix":"c7789c9e99af943d","entry":"weighted_l1_loss","repo":"Dayan-Guan/DA-VSN","repo_kind":"official","path":"davsn/domain_adaptation/train_video_UDA.py","file_url":"https://github.com/Dayan-Guan/DA-VSN/blob/HEAD/davsn/domain_adaptation/train_video_UDA.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c7789c9e99af943d"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}